The entire surface of the earth can be referenced according to geographic coordinates indicating a precise spatial location. The geographic features present at each spatial location may be modelled and stored in the form of a digital map or model. Such maps may contain detailed information relating to topographical features of a terrain surface, including natural features such as mountains, hills, valleys, rivers and lakes, as well as artificial features such as roads, buildings, and monuments. Further mapping information may include terrain or land relief, such as the relative elevation, slope and orientation of land features. A geographic or geospatial information system (GIS) refers to any system designed to capture, store, organize and present the assortment of information relating to geographic features designated by spatial coordinates. Users may interactively process, analyze and visualize the spatial information via a specific GIS application or program. These tools can be applied to a diverse range of technical fields, ranging from public infrastructure management (e.g., roads, water, sewage), and transportation planning, to natural resource exploration and environmental supervision (e.g., fire prevention, managing national disasters).
The geographical information may be digitally represented in the form of a vector data model or a raster data model. In a vector data representation, the features of a terrain surface are designated geometrically as points, lines and polygons. Each point feature is represented as a single coordinate pair, while line and polygon features are represented as ordered lists of vertices. Each vector feature is associated with attributes that describe certain properties or characteristics of the respective feature. The vector features are generally grouped into “layers”, where the features in a given layer include the same geometry type and the same kinds of attributes. For example, information about species of trees may be categorized in a “trees layer”, while the types and shapes of roads and streets may be found in a “roads layer”.
Raster data organizes the terrain surface as a grid of cells or pixels, where each pixel contains an attribute value and location coordinates. Unlike a vector structure, which stores coordinates explicitly, raster data coordinates are contained in the ordering of the grid or matrix. Groups of cells that share the same value represent the same type of geographic feature. A raster image may also be referred to as a bitmap or a pixel map. Whereas raster data is made up of evenly spaced pixels, the points (or lines or polygons) in a vector data model need not be evenly spaced. Vector models are typically more useful for representing regions that have a discrete boundary, such as a street or a city, whereas a raster model is preferable for representing continuously varying data, such as elevation data.
Vector models have certain advantages, such as the ability to represent data at its original resolution, while the image resolution is limited by the pixel size in raster data. However, there are also disadvantages associated with vector models. One issue is the considerable amount of information contained in a vector dataset, which includes numerous data types, values and formats. As a result, a GIS application that utilizes this information requires substantial processing for executing the necessary computing tasks, such as reading, filtering and displaying the vector data, which also places further constraints on the application designer. This problem is exacerbated for applications that need to respond in “real-time”, such as digital map displays for fighter pilots.
An additional problem is that vector data models are geographically non-uniform. For example, a desert or other barren land area will contain very little information concerning bodies of water, in contrast to a land area in a tropical climate which will have an abundance of bodies of water. This non-uniformity leads to difficulties when attempting to establish a “maximum limit” for the amount of data in each category (or vector layer) when formulating a GIS application. In particular, it is difficult for the application designer to allocate sufficient processing resources for displaying information in a selected category, since the amount of resources that will actually be required cannot be known in advance. This problem leads to prolonged loading times and delays when displaying vector layers, since the application encounters complications in retrieving and displaying the relevant information.
One technique for handling the aforementioned issues is to dilute or filter the collection of information stored in the vector data model. Data that is not directly utilized by the application can be removed before the application is executed. While this approach saves the application from dealing with extraneous data, it also requires pre-processing the information, further pre-processing prior to every update, and potentially eliminating information that may turn out to be useful, as well as providing a lack of uniformity in the distribution of the diluted information.
An alternative technique is to initially organize the vector data in a more efficient configuration. This generally involves modifying the format in which the data is stored, and creating a search tree data structure for locating specific data values more efficiently. Although the searching process may be improved with this approach, there also remains a substantial amount of extraneous data, and an expansion in the overall stored information due to the data-intensive search trees. The reorganized information is also distributed in a non-uniform manner.
Yet another approach is to convert the vector data model into a raster data format, using a suitable conversion application. A raster image allows displaying information in real-time, as the data set has a fixed size limit (the bitmaps are generally designated with a particular size for a given terrain area). However, some information may be lost in the conversion to a raster image, since the initial vector layers are no longer represented distinctly. Moreover, the raster data requires a large amount of disk space, and transferring the raster data from the disk to a local processing memory of the application to allow displaying can be quite time-consuming.
U.S. Pat. No. 5,715,331 to Hollinger, entitled: “System for generation of a composite raster-vector image”, is directed to an image processing system for creating a composite raster-vector image from a raster only image. Edges in a raster image are detected and used to create a partial vector image, which is then combined with the original raster image to create a composite image. The composite image can be manipulated without distortion. In order to display or print the image, the vector image data is rasterized and merged with the raster image data. The data in the raster image can be smoothed by eliminating information contained in the vector image.
U.S. Pat. No. 6,732,120 to Du, entitled: “System and method for processing and display of geographical data”, discloses a display system with a graphical display area of visual information depicting a map region of interest coordinated to a display area of textual information of groups of user defined categories. The user defined categories group objects having like attributes to form a collection of such objects. Each object of the group references a location in the geographic display area. The user defined categories are capable of further coordination with a user controllable tab list display area of information identifying particulars of each of the objects or members of the category group. In another aspect, geographic data defining a spatial extent is modelled and stored in a relational database. The spatial extent of a given hierarchical level is partitioned into cells, and a unique cell identifier or spatial index is assigned to each cell. Each geographic object is assigned a unique cell identifier defined by the cell in which the object is located. The geographic objects in each hierarchical level are grouped by their unique cell identifiers to form grouped objects, which are stored as a long binary field in a relational database record corresponding to the cell identifiers.
U.S. Pat. No. 7,684,612 to Berrill, entitled: “Method and apparatus for storing 3D information with raster imagery”, discloses a method for storing three-dimensional (3D) information within the pixel information of an image. A plurality of images is acquired and combined to form a single image, and X-parallax information is stored with the pixel data of the single image. In particular, an X-parallax value is stored between stereoscopic images with the typical pixel information, such as by increasing the pixel depth, so that only a single image needs to be stored, whether a mosaic of multiple images or a partial image, to allow for proper reconstruction of 3D objects.
U.S. Pat. No. 7,933,451 to Kloer, entitled: “Feature extraction using pixel-level and object-level analysis”, discloses an image processing method for extracting a feature from a GIS digital image. A pixel-level cue algorithm is executed to identify an interesting area of a raster image depicting physical objects. A pixel-level probability that the identified interesting area represents the feature is determined, using a result from the pixel-level cue algorithm. The pixel-level probability is compared to a pixel-level cue threshold. If the pixel-level probability satisfies the pixel-level threshold, then a portion of the raster image is converted to a vector layer, an object-level cue algorithm is executed on the vector layer to identify an interesting area, an object-level probability is determined that the identified interesting area is the feature using a result from the pixel-level cue algorithm, and the object-level probability is compared to an object-level cue threshold.
U.S. Pat. No. 9,171,485 to Gautama et al, entitled: “Geodatabase information processing”, discloses a system for updating and/or extending geodatabases with geo-information. An input means obtains vector-based data from location aware devices, and a data processor inserts information from the vector-based data into a raster-based data structure comprising data container elements corresponding with topologically arranged locations. A data voting unit adds information from the vector-based data individually to selected data container elements. The data processor derives geo-information based on the raster-based data structure and the inserted information from the vector-based data.